Poverty Assessment and Mapping in Sudan Part 3
Poverty Assessment in Sudan: Mapping natural resource potential
Eddy De-Pauw, Weicheng Wu
Agricultural Economics and Policy Research Centre (AEPRC), Agricultural Research CorporaƟon (ARC), Sudan Poverty Assessment in Sudan Mapping natural resource potenƟal Copyright © 2012 ICARDA (Interna onal Center for Agricultural Research in the Dry Areas).
Cita on: Eddy De-Pauw, Weicheng Wu 2012. Poverty Assessment in Sudan Mapping natural resource poten al. ICARDA, Aleppo, Syria. vi + 33 pp.
ISBN: 92-9127-263-9
Interna onal Center for Agricultural Research in the Dry Areas (ICARDA) P.O.Box 5466, Aleppo, Syria Tel: +963 21 2213433, 2213477, 2225012 E – mail: [email protected] Website: www.icarda.org
The views expressed are those of the authors, and not necessarily those of ICARDA. Where trade names are used, it does not imply endorsement of, or discrimina on against, any product by the Center. Maps have been used to support research data, and are not intended to show poli cal boundaries.
ii Contents Excu ve Summary ...... v
Mapping Agricultural Resource Poten al of Sudan ...... 1 1. Agroclima c Zones ...... 1 2. Annual Growings Degree Days ...... 3 3. Annual Aridity Index ...... 5 4. Biomass ...... 7 5. Clima cally Determined Biomass Produc vity Index (CDBPI) ...... 9 6. Eleva on ...... 11 7. Slopes ...... 11 8. Landforms ...... 11 9. Annual Precipita on ...... 15 10. Annual Poten al Evapotranspira on (PET) ...... 17 11. Length of Growing Period (LGP) ...... 19 12. Clima c Resource Index (CRI) ...... 21 13. Soil Resource Index (SRI) ...... 23 14. Topographic Resource Index (TRI) ...... 25 15. Agricultural Resource Poten al Index (ARI) ...... 27 16. Drought in Sudan ...... 29
Drought Pa erns by State ...... 29
iii
EXECUTIVE SUMMARY
Mapping of agricultural resource potenƟal of North and Southern Sudan – Part 3.
Sudan Rural Poverty Analysis This is Part 3 of a study, presented in three reports that detail the results of a poverty assessment and mapping project in North and Southern Sudan. The study’s objec ve was to produce a rural poverty analysis and poverty maps for North and Southern Sudan, and based on these findings, recommend agricultural interven ons that can help reduce poverty. These findings provided an input to the IFAD Sudan Country Program 2007–2012, that takes into considera on the new cons tu onal changes in Sudan resul ng from the peace agreements with South/ East/West Sudan and to support peace, security and stability in Sudan. o Poverty assessment in Northern Sudan – Part 1 o Poverty assessment in Southern Sudan – Part 2 o Mapping of agricultural resource poten al of North and Southern Sudan – Part 3.
Mapping of agricultural resource poten al of North and Southern Sudan This sec on provides detailed maps of different agro – ecological, clima c, and soil indices. These have been combined into agricultural resource poten al indices.
Key findings of the assessment: General state of the economy and agriculture Sudan’s economic structure has undergone a major shi over the past two decades (DTIS 2008), the main drivers of this change are the discovery of oil in the early 2000s and the expansion in services dominated by telecommunica ons, transport, and construc on. Agriculture used to be the leading economic sector, forming typically more than 40% of GDP, but has lost much ground with a drop of its GDP share to 33% in 2007. A more drama c trend has been the deteriora on in the contribu on of agriculture to the country’s exports, declining to some 3% in 2007 down from an average of 74% in the 1996–1998 period. Both the rela ve share and the absolute value of agricultural exports has declined. Data from the Central Bank of Sudan reveals an annual trend value of $71,500.
Both income poverty and general human poverty are concerns for North and Southern Sudan. There is considerable depriva on in educa on and health, and poor households are par cularly disadvantaged. Yet, despite the current fragile situa on of Sudan’s agriculture, this study found that the countries have enormous poten al to raise crop yields by bridging at least part of its current ‘yield gaps’ – between actual and poten al food produc on. These vary from 46% to as high as 566% between on – farm trials and prevailing commercial produc vity. Irrigated crops can be improved by margins ranging from about 50% to > 140%. Even higher yield poten al have been iden fied for rainfed crops – where poten al margins ranged from twofold to over fivefold.
Prerequisites for achieving these levels of development and macroeconomic stability require an ambi ous development plan that includes: crea on of a sound financial system and an efficient federal system through more decentraliza on, coupled with adequate financial and technical resources and par cipatory mechanisms, and the just income and wealth distribu on.
Northern Sudan assessment: Key findings and recommendaƟons (see Part 1) The results of the Northern Sudan Poverty Assessment show higher rural than urban poverty, in the six regions studied. This rural–urban disparity was mainly due to the rural–urban differences in food composi ons and food prices. However, in absolute terms the number of rural poor was greater than of urban poor. Higher poverty incidence in rural areas is a due to chronic low produc vity and low income in rural areas.
v A targe ng procedure conducive to poverty reduc on in the Sudan is proposed in a chart (see the recommenda on at the end of the Northern Sudan report), which suggests priority agricultural interven ons in the 10 states with both highest income poverty and human poverty levels.
Southern Sudan Study – Key findings and recommendaƟons (Part 2) The survey es mates income poverty incidence at 99.6% in the states of Eastern Equatoria State, 88.6% in the Lakes State, and 54.0% in Central Equatoria State. The situa on was especially serious in Eastern Equatoria and Lakes States. The study also showed acute shor alls of the required caloric intake for about a third of both Eastern Equatoria and Central Equatoria States. Some 60% of the popula on in Lakes State faces a shor all in the required daily caloric food intake.
This is an indica on of deep poverty among a sizeable por on of the popula on. Lakes State had the lowest per capita income from both agricultural and non-agricultural sources. In all states, poverty was lower when expenditure es mates were used than when income es mates were used. This is a common feature in poverty analysis, and it is generally believed that expenditures are more easily recalled than incomes, but the ranking of rela ve poverty by province did not change.
To address this acute situa on a set of 14 recommenda ons is proposed. The government will need to implement a long – term poverty reduc on strategy that takes a broad perspec ve – focusing on strengthening its ins tu ons, developing and implemen ng policies and legisla on, inves ng in related areas of research and infrastructure to link rural communi es to economic centers building capacity, systems, and structures for delivering services in the areas of health, educa on, and clean water. Ac ons for donors and other partners such as the private sector are also specified. MAPPING AGRICULTURAL RESOURCE POTENTIAL OF SUDAN Eddy De Pauw and Weicheng Wu The maps listed in the following paragraphs have been prepared as part of the project. Each of these maps helps to characterize the poten al and risks related to the natural resource base for agriculture in the different States of Sudan.
1. AGROCLIMATIC ZONES A classifica on of climates in accordance with the UNESCO classifica on for the arid zones provides evidence that the climates of Sudan are very diverse, and differ mainly in their moisture characteris cs, and less in their temperature regime. The explana on of the different climates is given in (Fig. 1), the agro clima c zones map is shown in (Fig. 2).
Figure1. Legend of the agro clima c zones map
1 Figure2. Agroclima c zones map
2 2. ANNUAL GROWING DEGREE DAYS The map provides the mean temperature summed over the whole year. This value is an indicator of either the temperature adequacy or constraints for plant biomass produc on (> 10 000 growing degree – days poses serious risk of heat stress). Generally in Sudan, temperatures are high and do not show much spa al varia on, due to Sudan’s posi on within the tropics and the lack of high mountain areas. The distribu on of annual growing degree – day classes by state is summarized in (Table 1). The map of annual growing degree days is shown in (Fig. 3).
Table 1: Annual growing degree days: percentage of the states of Sudan in each class
Growing degree – day classes (°C.days) State <5000 5000 – 6000 – 7000 – 8000 – 9000 – 10000 – 6000 7000 8000 9000 10000 11000 White Nile 0 0 0 0 0 0 100 South Kordofan 0 0 0 0 0 44 55 North Kordofan 0 0 0 0 0 48 52 Sennar 0 0 0 0 0 0 100 Red Sea 0 0 0 1 13 51 35 Northern 0 0 0 0 10 52 38 Nile 0 0 0 0 0 15 85 Khartoum 0 0 0 0 0 0 100 Kassala 0 0 0 0 0 1 99 Gedaref 0 0 0 0 0 1 99 Blue Nile 0 0 0 0 1 35 65 Gezira 0 0 0 0 0 0 100 West Darfur 0 0 0 2 27 70 0 South Darfur 0 0 0 1 8 84 8 North Darfur 0 0 0 0 19 80 0 Lakes 0 0 0 0 0 86 14 Bahr El Jabal 0 0 0 0 20 80 1 Eastern Equatoria 0 0 1 3 16 61 19 Upper Nile 0 0 0 0 0 3 97 Western Bahr El Jabal 0 0 0 0 3 97 0 Unity 0 0 0 0 0 2 98 Northern Bahr El Jabal 0 0 0 0 0 100 0 Jonglei 0 0 0 0 2 12 86 Warrab 0 0 0 0 0 43 57 Western Equatoria 0 0 0 0 2 98 0
[Note: in all tables pink color denotes where >10% of the given class occurs]
3 Figure3. Map of annual growing degree days
4 3. ANNUAL ARIDITY INDEX The annual aridity index is the ra o of annual precipita on over the annual poten al evapotranspira on. It provides a measure of the poten al of climate to sa sfy the water demand of plants by considering both the water supply from precipita on and the water demand by evapotranspira on. Sudan shows a very high range in aridity index, from hyper-arid to perhumid, and this is one of the key drivers in determining agricultural poten al in rainfed agriculture. The distribu on of aridity index classes by state is summarized in (Table 2). The map of the annual aridity index map is shown in (Fig. 4(.
Table 2: Annual Aridity Index: percentage of the States of Sudan in each class
Aridity class State Hyper – arid Arid Semi – arid Sub – humid Humid Per – humid South Kordofan 0 17 83 0 0 0 North Kordofan 0 73 27 0 0 0 Sennar 0 50 50 0 0 0 Red Sea 49 45 6 0 0 0 Northern 79 21 0 0 0 0 Nile 90 10 0 0 0 0 Khartoum 27 73 0 0 0 0 Kassala 0 100 0 0 0 0 Gedaref 0 44 56 0 0 0 Blue Nile 0 0 76 23 1 0 Gezira 0 100 0 0 0 0 West Darfur 0 15 85 0 0 0 South Darfur 0 3 84 13 0 0 North Darfur 35 49 16 0 0 0 Bahr El Jabal 0 0 12 65 24 0 Eastern Equatoria 0 2 39 54 5 0 Upper Nile 0 3 90 6 1 0 Western Bahr El Jabal 0 0 67 33 0 0 Unity 0 0 100 0 0 0 Northern Bahr El Jabal 0 0 50 50 0 0 Jonglei 0 0 87 13 0 0 Warrab 0 0 52 48 0 0 Western Equatoria 0 0 0 74 26 0
5 Figure 4. Map of the annual aridity index
6 4. BIOMASS Vegeta on biomass is obviously a very direct indicator of natural resource poten al (Fig. 2). This map is the result of a new study to assess the amount of aboveground biomass by integra ng the image analysis of satellite imagery at different resolu ons (using MODIS, Landsat, and Google Earth) with the Land Cover map of Sudan. The biomass is expressed in metric tons/ha. The distribu on of biomass classes by state is summarized in (Table 3). The biomass map is shown in (Fig. 5).
Table 3. Above-ground biomass: percentage of the states of Sudan in each class
Biomass class (ton/ha) State 0 – 0.5 0.5 – 1 1 -2 2 – 5 5 – 10 10 – 20 20 – 43 White Nile 31 13 19 19 6 11 0 South Kordofan 3 3 4 14 24 52 0 North Kordofan 60 8 14 15 2 1 0 Sennar 47 7 3 9 14 20 0 Red Sea 99 0 0 0 0 0 0 Northern 100 0 0 0 0 0 0 Nile 99 1 0 0 0 0 0 Khartoum 93 5 2 1 0 0 0 Kassala 49 10 13 15 8 5 0 Gedaref 39 5 9 26 10 10 0 Blue Nile 13 6 0 5 43 32 0 Gezira 76 7 7 8 2 1 0 West Darfur 4 1 5 33 12 45 0 South Darfur 6 2 10 23 21 38 0 North Darfur 76 6 9 7 1 1 0 Lakes 0 2 0 7 6 82 3 Bahr El Jabal 0 4 1 4 6 81 4 Eastern Equatoria 0 1 2 18 15 61 2 Upper Nile 2 0 0 9 30 58 0 Western Bahr El Jabal 0 0 0 3 19 74 3 Unity 0 0 1 20 15 63 0 Northern Bahr El Jabal 3 1 0 2 17 75 2 Jonglei 0 0 1 10 8 80 0 Warrab 2 1 1 11 6 79 0 Western Equatoria 0 2 1 2 6 71 19
7 Figure 5. Biomass map
8 5. CLIMATICALLY DETERMINED BIOMASS PRODUCTIVITY INDEX (CDBPI) While the vegeta on biomass shows the actual totals, climate ul mately determines the poten al to produce the biomass. For comparison of the clima c poten al across large areas we use a very simple indicator, the product of the annual total growing degree – days (an indicator of temperature adequacy) with the aridity index (an indicator of moisture sufficiency). As the range in annual growing degree – days was 8000–11 000, the main factor that affects the produc vity of climate for vegeta on biomass produc on in Sudan is moisture. The distribu on of CDBPI by state is summarized in (Table 4). The map of the Clima cally Determined Biomass Produc vity Index (CDBPI) is shown in (Fig. 6)
Table 4: Clima cally determined biomass produc vity index (CDBPI): percentage of the States of Sudan in each class CDBPI State 0 – 100 – 200 – 500 – 1000 – 2000 – 3000 – 4000 – 5000 – 6000 – 7000 – 100 200 500 1000 2000 3000 4000 5000 6000 7000 8000 White Nile 0 0 9 37 54 0 0 0 0 0 0 South Kordofan 0 0 0 0 27 65 8 0 0 0 0 North Kordofan 0 6 45 32 17 0 0 0 0 0 0 Sennar 0 0 0 1 66 30 2 0 0 0 0 Red Sea 11 19 57 12 1 0 0 0 0 0 0 Northern 80 18 2 0 0 0 0 0 0 0 0 Nile 34 22 42 2 0 0 0 0 0 0 0 Khartoum 0 7 59 34 0 0 0 0 0 0 0 Kassala 0 0 25 63 12 0 0 0 0 0 0 Gedaref 0 0 0 11 51 35 3 0 0 0 0 Blue Nile 0 0 0 0 0 51 46 4 0 0 0 Gezira 0 0 1 80 19 0 0 0 0 0 0 West Darfur 0 0 0 6 55 39 0 0 0 0 0 South Darfur 0 0 0 2 48 38 9 3 0 0 0 North Darfur 23 24 19 27 7 0 0 0 0 0 0 Lakes 0 0 0 0 0 0 22 57 20 0 0 Bahr El Jabal 0 0 0 0 0 0 0 39 43 19 0 Eastern Equatoria 0 0 0 0 0 15 42 25 16 2 0 Upper Nile 0 0 0 0 8 35 57 1 0 0 0 Western Bahr El Jabal 0 0 0 0 0 7 18 44 31 0 0 Unity 0 0 0 0 0 1 99 0 0 0 0 Northern Bahr El Jabal 0 0 0 0 0 4 75 21 0 0 0 Jonglei 0 0 0 0 0 1 77 21 1 0 0 Warrab 0 0 0 0 0 0 50 44 6 0 0 Western Equatoria 0 0 0 0 0 0 0 5 39 40 15
9 Figure6. Clima cally Determined Biomass Produc vity Index (CDBPI)
10 6. ELEVATION This map is self – explanatory. In contrast with other countries in the region, e.g. Ethiopia or Eritrea, Sudan has limited differences in eleva on, except for a few mountain areas such as Jebel Marra, Nuba, Red Sea, and Imatong Mountains, which explains the limited range in temperature. Higher eleva ons are useful in this context, especially when moisture is not too limi ng; as this would be a factor enabling some biodiversity or special crops, such as fruit trees, to thrive. An eleva on map is shown in (Fig. 7).
7. SLOPES The slopes were determined from the Shu le Radar Topographic Mission (SRTM) digital eleva on model. Sudan is generally flat, with slopes < 5%, except in the few mountain areas of Jebel Marra, Red Sea, Nuba, and Imatong Mountains in the south. A slope map is shown in (Fig. 8).
8. LANDFORMS Using eleva on and slope as differen a ng criteria, a 15-class landform classifica on was established (see legend in Fig. 9). These were condensed into four simplified landforms. The distribu on of these simplified landforms (plains, low hills, steep hills, and mountains) is summarized in (Table 5). A map of the landforms is shown in (Fig. 9).
Table 5: Major landforms: percentage of the States of Sudan in each class Landform classes State Plains Low hills Steep hills Mountains South Kordofan 100 0 0 0 North Kordofan 100 0 0 0 Sennar 98 1 1 0 Red Sea 81 3 9 7 Northern 96 3 2 0 Nile 94 4 2 0 Khartoum 97 2 0 0 Kassala 85 6 6 3 Gedaref 96 3 1 0 Blue Nile 78 9 12 2 Gezira 100 0 0 0 West Darfur 91 4 4 0 South Darfur 81 10 7 3 North Darfur 93 4 3 0 Bahr El Jabal 87 10 3 0 Eastern Equatoria 72 11 11 7 Upper Nile 92 2 4 1 Western Bahr El Jabal 94 2 4 0 Unity 100 0 0 0 Northern Bahr El Jabal 100 0 0 0 Jonglei 93 3 5 0 Warrab 100 0 0 0 Western Equatoria 97 2 0 0
11 Figure7. Eleva on map
12 Figure8. Slope map
13 Figure9. Map of landforms
14 9. ANNUAL PRECIPITATION This map is self-explanatory, with units of millimeters. The distribu on of precipita on classes by state is summarized in (Table 6). An annual precipita on map is shown in (Fig. 10).
Table 6: Annual precipita on: percentage of the States of Sudan in each class
Annual precipitaƟon class (mm) State 0–50 50– 100– 200– 300– 400– 500– 600– 800– 1000– 1200– 100 200 300 400 500 600 800 1000 1200 1400 White Nile 0 3 37 45 15 0 0 0 0 0 0 South Kordofan 0 0 0 1 17 30 38 14 0 0 0 North Kordofan 6 32 37 22 3 0 0 0 0 0 0 Sennar 0 0 0 18 38 24 13 6 0 0 0 Red Sea 32 45 21 1 0 0 0 0 0 0 0 Northern 98 2 0 0 0 0 0 0 0 0 0 Nile 58 37 5 0 0 0 0 0 0 0 0 Khartoum 7 43 50 0 0 0 0 0 0 0 0 Kassala 0 10 77 14 0 0 0 0 0 0 0 Gedaref 0 0 9 22 26 19 20 5 0 0 0 Blue Nile 0 0 0 0 0 18 29 48 5 0 0 Gezira 0 0 55 44 0 0 0 0 0 0 0 West Darfur 0 0 1 15 26 41 16 1 0 0 0 South Darfur 0 0 0 20 22 24 22 11 2 0 0 North Darfur 46 13 28 9 2 1 0 0 0 0 0 Lakes 0 0 0 0 0 0 0 57 37 6 0 Bahr El Jabal 0 0 0 0 0 0 0 15 45 32 9 Eastern Equatoria 0 0 0 0 0 1 11 52 20 15 0 Upper Nile 0 0 0 0 7 13 20 60 1 0 0 Western Bahr El Jabal 0 0 0 0 0 0 5 22 64 9 0 Unity 0 0 0 0 0 0 0 100 0 0 0 Northern Bahr El Jabal 0 0 0 0 0 0 1 85 14 0 0 Jonglei 0 0 0 0 0 0 2 87 10 1 0 Warrab 0 0 0 0 0 0 0 69 31 0 0 Western Equatoria 0 0 0 0 0 0 0 0 19 59 22
15 Figure10. Annual precipita on map
16 10. ANNUAL POTENTIAL EVAPOTRANSPIRATION (PET) This map shows the water demand of a reference crop (grass) on an annual basis, calculated according to the Penman–Monteith method (expressed in mm). The distribu on of PET classes by state is summarized in (Table 7). An annual poten al evapotranspira on map is shown in (Fig. 11).
Table 7: Annual poten al evapotranspira on (PET): percentage of the States of Sudan in each class
Annual PET (mm) State 1000– 1200– 1400– 1600– 1800– 2000– 2200– 2400– 2600– 1200 1400 1600 1800 2000 2200 2400 2600 2750 White Nile 0 0 0 0 0 36 54 10 0 South Kordofan 0 0 0 0 0 87 13 0 0 North Kordofan 0 0 0 0 0 1 62 37 0 Sennar 0 0 0 0 0 49 51 0 0 Red Sea 0 0 0 0 2 30 30 38 0 Northern 0 0 0 0 0 0 11 47 42 Nile 0 0 0 0 0 0 0 83 17 Khartoum 0 0 0 0 0 0 0 98 2 Kassala 0 0 0 0 0 18 38 44 0 Gedaref 0 0 0 0 0 23 72 5 0 Blue Nile 0 0 0 0 9 89 2 0 0 Gezira 0 0 0 0 0 0 53 47 0 West Darfur 0 0 0 0 16 72 11 0 0 South Darfur 0 0 0 6 20 58 16 0 0 North Darfur 0 0 0 0 1 24 48 27 0 Lakes 0 0 0 33 67 0 0 0 0 Bahr El Jabal 0 0 1 96 4 0 0 0 0 Eastern Equatoria 0 1 4 28 51 15 0 0 0 Upper Nile 0 0 0 0 3 97 0 0 0 Western Bahr El Jabal 0 0 0 47 49 4 0 0 0 Unity 0 0 0 0 60 40 0 0 0 Northern Bahr El Jabal 0 0 0 0 93 7 0 0 0 Jonglei 0 0 0 3 83 15 0 0 0 Warrab 0 0 0 14 85 1 0 0 0 Western Equatoria 0 0 10 90 0 0 0 0 0
17 Figure11. Annual poten al evapotranspira on map
18 11. LENGTH OF GROWING PERIOD (LGP) The LGP is the period of the year (in days) in which neither moisture nor temperature constrains plant growth. As temperature is not a limi ng factor in Sudan (except at high temperatures), moisture availability is the key constraint. The moisture – limited growing period is calculated as the con guous period in which the ra o of actual to poten al evapotranspira on, as calculated by simple water balance, is > 50%. The distribu on of LGP classes by state is summarized in (Table 8). The map of the length of growing period is shown in (Fig. 12). Table 8: Length of growing period: percentage of the States of Sudan in each class LGP (days) State 0–30 30– 60– 90– 120– 150– 180– 210– 240– 270– 300– 330– 60 90 120 150 180 210 240 270 300 330 365 White Nile 35 35 30 0 0 0 0 0 0 0 0 0 South Kordofan 0 0 10 34 40 16 0 0 0 0 0 0 North Kordofan 67 20 13 0 0 0 0 0 0 0 0 0 Sennar 0 9 34 38 15 4 0 0 0 0 0 0 Red Sea 100 0 0 0 0 0 0 0 0 0 0 0 Northern 100 0 0 0 0 0 0 0 0 0 0 0 Nile 100 0 0 0 0 0 0 0 0 0 0 0 Khartoum 100 0 0 0 0 0 0 0 0 0 0 0 Kassala 89 11 0 0 0 0 0 0 0 0 0 0 Gedaref 12 14 30 27 13 2 0 0 0 0 0 0 Blue Nile 0 0 0 11 32 47 9 0 0 0 0 0 Gezira 59 41 0 0 0 0 0 0 0 0 0 0 West Darfur 0 5 16 38 40 0 0 0 0 0 0 0 South Darfur 0 2 24 26 27 20 2 0 0 0 0 0 North Darfur 78 15 5 2 0 0 0 0 0 0 0 0 Lakes 0 0 0 0 0 2 62 29 6 0 0 0 Bahr El Jabal 0 0 0 0 0 0 14 32 51 2 0 0 Eastern Equatoria 13 14 3 0 1 16 19 10 23 1 0 0 Upper Nile 0 0 2 12 15 63 8 0 0 0 0 0 Western Bahr El Jabal 0 0 0 0 1 24 44 31 0 0 0 0 Unity 0 0 0 0 0 92 8 0 0 0 0 0 Northern Bahr El Jabal 0 0 0 0 0 69 31 0 0 0 0 0 Jonglei 0 0 0 0 0 43 51 4 1 0 0 0 Warrab 0 0 0 0 0 43 44 13 0 0 0 0 Western Equatoria 0 0 0 0 0 0 0 23 73 5 0 0
19 Figure12. Length of growing period map
20 12. CLIMATIC RESOURCE INDEX (CRI) The CRI is an index of scale 0–100 for capturing the clima c poten al for biomass produc on. It is based on the CDBPI. The distribu on of CRI classes by state is summarized in (Table 9). The clima c resource index map is shown in (Fig. 13).
Table 9: Clima c Resource Index under Rainfed and Irrigated condi ons (CRI – RF/IR): percentage of the States of Sudan in each class
CRI (0 – 100) State 0 – 10 10–20 20– 30–40 40–50 50– 60–70 70–80 80–90 90– 30 60 100 White Nile 24 53 16 0 0 0 0 0 6 0 South Kordofan 0 2 30 46 19 0 0 0 3 1 North Kordofan 65 32 2 0 0 0 0 0 1 1 Sennar 0 17 56 14 5 0 0 0 8 0 Red Sea 95 4 0 0 0 0 0 0 0 0 Northern 100 0 0 0 0 0 0 0 0 0 Nile 98 0 0 0 0 0 0 0 1 0 Khartoum 95 2 0 0 0 0 0 0 3 0 Kassala 58 37 0 0 0 0 0 0 5 0 Gedaref 1 27 37 28 5 0 0 0 2 0 Blue Nile 0 0 0 40 37 20 2 0 1 0 Gezira 19 57 0 0 0 0 0 0 25 0 West Darfur 0 23 44 32 0 0 0 0 0 1 South Darfur 0 23 30 29 10 5 1 0 1 1 North Darfur 77 20 2 0 0 0 0 0 0 0 Lakes 0 0 0 0 1 39 38 17 5 0 Bahr El Jabal 0 0 0 0 0 3 32 30 26 9 Eastern Equatoria 0 0 0 9 27 29 16 11 9 0 Upper Nile 0 0 9 18 63 8 0 0 1 0 Western Bahr El Jabal 0 0 0 3 12 14 33 34 3 0 Unity 0 0 0 0 72 28 0 0 0 0 Northern Bahr El Jabal 0 0 0 0 46 43 11 0 0 0 Jonglei 0 0 0 0 38 51 10 1 0 0 Warrab 0 0 0 0 13 52 27 9 0 0 Western Equatoria 0 0 0 0 0 0 3 18 41 38
21 Figure13. Clima c Resource Index (CRI) map
22 13. SOIL RESOURCE INDEX (SRI) The SRI is the propor on of a pixel on a GIS map without problema c soil types. Problema c soils are those that are either unsuitable for agricultural produc on due to severe physical limita ons, or soils that are very expensive to reclaim for produc on. The distribu on of SRI classes by state is summarized in (Table 10). The soil resource index map is shown in (Fig. 14). Table 10: Soil Resource Index (SRI): percentage of the States of Sudan in each class
SRI (0 – 100) State 0–10 10– 20– 30–40 40– 50– 60–70 70–80 80–90 90– 20 30 50 60 100 White Nile 34 0 0 9 2 0 0 0 9 46 South Kordofan 23 0 0 0 17 0 0 20 0 41 North Kordofan 15 4 0 39 38 0 0 3 0 2 Sennar 0 0 0 0 0 0 0 0 7 93 Red Sea 2 20 0 31 2 0 25 3 7 10 Northern 11 75 0 7 1 0 5 0 1 0 Nile 4 42 0 0 39 0 8 1 4 2 Khartoum 16 0 0 0 49 0 0 0 7 29 Kassala 0 0 0 0 4 0 0 0 3 93 Gedaref 0 0 0 0 0 0 0 0 31 68 Blue Nile 2 0 0 0 0 0 4 0 23 71 Gezira 0 0 0 0 0 0 0 0 8 92 West Darfur 4 1 0 4 52 0 17 0 0 21 South Darfur 24 0 16 13 4 0 22 18 0 3 North Darfur 39 28 0 3 26 0 1 1 0 1 Lakes 0 0 0 30 0 0 0 25 0 45 Bahr El Jabal 0 0 37 31 0 0 0 1 2 29 Eastern Equatoria 1 0 1 0 6 0 13 1 13 65 Upper Nile 0 0 0 0 0 0 1 0 3 96 Western Bahr El Jabal 0 0 0 47 0 0 0 51 0 2 Unity 0 0 0 0 0 0 0 11 0 89 Northern Bahr El Jabal 0 0 0 0 0 0 0 91 0 9 Jonglei 0 0 1 0 0 0 0 0 2 96 Warrab 1 0 0 11 0 0 0 26 0 62 Western Equatoria 4 0 14 82 0 0 0 0 0 1
23 Figure14. Soil Resource Index (SRI) map
24 14. TOPOGRAPHIC RESOURCE INDEX (TRI) The TRI is the propor on of the pixel without problema c soil types. The TRI was derived from a Shu le Radar Topographic Mission Digital Eleva on Model (CGIAR–CSI online database) by calcula ng the slope for each 90 – m pixel. To match the resolu on of the other datasets used to calculate the CRI and SRI, the slope pixels were aggregated to a pixel – size 100 Times larger. TRI was then calculated as the percentage of SRTM pixels with slope ≥ 15%, which corresponds well with the slope – limit delinea ng what cons tutes sustainable agriculture without resor ng to use of terracing. The distribu on of TRI classes by state is summarized in (Table 11). The Topographic Resource Index (TRI) map is shown in (Fig. 15).
Table 11: Topographic Resource Index (TRI): percentage of the States of Sudan in each class
TRI (0 – 100) State 0–10 10–20 20– 30–40 40– 50– 60–70 70–80 80–90 90– 30 50 60 100 White Nile 0 0 0 0 0 0 0 0 0 100 South Kordofan 0 0 0 1 1 1 1 1 1 95 North Kordofan 0 0 0 0 0 0 0 0 0 99 Sennar 0 0 0 0 0 0 0 0 0 100 Red Sea 4 3 3 3 3 3 3 4 5 70 Northern 0 0 0 0 0 0 0 1 1 98 Nile 0 0 0 0 0 0 0 1 1 97 Khartoum 0 0 0 0 0 0 0 0 0 99 Kassala 0 0 0 0 0 0 0 0 0 98 Gedaref 0 0 0 0 0 0 0 0 0 99 Blue Nile 1 1 1 1 1 1 1 1 1 93 Gezira 0 0 0 0 0 0 0 0 0 100 West Darfur 0 1 1 1 1 1 2 2 3 87 South Darfur 0 0 0 0 0 0 0 0 1 97 North Darfur 0 0 0 0 0 0 1 1 1 96 Lakes 0 0 0 0 0 0 0 0 0 100 Bahr El Jabal 0 0 0 0 0 1 1 1 2 94 Eastern Equatoria 4 2 2 1 1 1 1 2 2 83 Upper Nile 0 0 0 0 0 0 0 0 0 99 Western Bahr El Jabal 0 0 0 0 0 0 0 0 0 99 Unity 0 0 0 0 0 0 0 0 0 100 Northern Bahr El Jabal 0 0 0 0 0 0 0 0 0 100 Jonglei 0 0 0 0 0 0 0 0 0 98 Warrab 0 0 0 0 0 0 0 0 0 100 Western Equatoria 0 0 0 0 0 0 0 0 1 98
25 Figure15. Topographic Resource Index (TRI) map
26 15. AGRICULTURAL RESOURCE POTENTIAL INDEX (ARI) The thema c indices (CRI, SRI, and TRI) were combined as raster themes in GIS, with the same spa al scope and resolu on, into the ARI. The ARI is an integrated index based on the clima c, soil, topographic, and water resources and is calculated as the lowest value of CRI, SRI, and TRI for rainfed areas. If, however, a frac on of the pixel is irrigated, only CRI is considered for the irrigated frac on in the ARI calcula on, assuming that irriga on takes place where soil and topographic condi ons are not severely constraining. The distribu on of ARI classes by state is summarized in (Table 12). The map of the agricultural resource poten al index is shown in (Fig. 16).
Table 12: Agricultural Resource Poten al Index (ARI): percentage of the States of Sudan in each class
ARI (0 – 100) State 0–10 10– 20– 30–40 40– 50– 60–70 70–80 80–90 90– 20 30 50 60 100 White Nile 53 26 16 0 0 0 0 0 4 0 South Kordofan 23 2 20 36 18 0 0 1 0 0 North Kordofan 76 23 1 0 0 0 0 0 0 0 Sennar 0 17 56 14 5 0 0 0 8 0 Red Sea 97 2 0 0 0 0 0 0 0 0 Northern 100 0 0 0 0 0 0 0 0 0 Nile 98 0 0 0 1 0 0 0 1 0 Khartoum 96 2 0 0 0 0 0 0 3 0 Kassala 58 37 0 0 0 0 0 0 5 0 Gedaref 1 27 37 28 5 0 0 0 2 0 Blue Nile 3 1 1 39 35 19 1 0 1 0 Gezira 19 57 0 0 0 0 0 0 25 0 West Darfur 5 24 40 31 0 0 0 0 0 0 South Darfur 24 11 28 20 10 5 2 0 0 0 North Darfur 90 8 2 0 0 0 0 0 0 0 Lakes 0 0 0 30 1 39 31 0 0 0 Bahr El Jabal 0 0 37 31 0 3 26 2 2 0 Eastern Equatoria 5 2 3 10 28 28 13 6 6 0 Upper Nile 0 0 9 18 62 8 0 0 1 0 Western Bahr El Jabal 0 0 0 47 11 14 26 1 0 0 Unity 0 0 0 0 72 28 0 0 0 0 Northern Bahr El Jabal 0 0 0 0 46 43 11 0 0 0 Jonglei 0 0 1 0 37 51 9 1 0 0 Warrab 1 0 0 11 13 51 24 0 0 0 Western Equatoria 4 0 14 82 0 0 0 0 0 0
27 Figure16. Agricultural Resource poten al Index (ARI) map
28 16. DROUGHT IN SUDAN The Standardized Precipita on Index (SPI) is a tool for monitoring drought and anomalously wet events based on a cumula ve probability distribu on of precipita on me series. Using a me series of monthly precipita on data from Sudan, the annual SPI has been calculated from 1921 to 1993, a period in which the number of meteorological sta ons in the country expanded from 50 to 120 and declined a erwards to 30. By interpola ng sta on SPI values and using a mask to cover hyper-arid areas, 73 annual SPI maps were created which allow to characterize historical drought pa erns and to compare the extent of drought in the different states of Sudan. The Drought Extent Index, defined as the average percentage area that would be affected by drought each year, varies between States between 5-11%, a surprisingly narrow range indica ng that drought is a feature of all States, with Southern and Western Darfur the most vulnerable ones. SPI trend analysis indicates that droughts became both more extensive and severe towards the end of the period and that wetness anomalies have declined. Droughts during the mid-1920s, par cularly in southern Sudan, were followed between 1930 and 1960 by a period characterized by normal or even above normal precipita on across the country, interrupted by local and sca ered drought events. This period of rela ve clima c stability was followed by an up ck in droughts during the 1960s-70s and culminated in a new state of the climate characterized by mul -year regional droughts from 1982 onwards.
Extent of drought in Sudan (% of country affected) 70 60
50
40
30
20
10
0 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
The drought pa erns of the period 1921-1993 are indica ve of the increases in precipita on variability to be expected under global warming. The SPI mapping is in agreement with field and satellite data and can be a useful tool for comparing longer-term vulnerability to drought within countries.
DROUGHT PATTERNS BY STATE
100 100 90 90 80 80 70 70 60 60 50 50 Percent 40 Percent 30 40 20 30 10 20 0 10 1920 1930 1940 1960 1970 1980 1990 2000 0 Year 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year
Area (%) affected by drought in White Nile State Area (%) affected by drought in South Kordofan State
29 100 100 90 90 80 80 70 70 60 60 50 50 Percent Percent 40 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought in North Kordofan Area (%) affected by drought in Sennar State State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought in Red Sea State Area (%) affected by drought in Northern State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought in Nile State Area (%) affected by drought : Khartoum State
30 100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year
Area (%) affected by drought : Kassala State Area (%) affected by drought : Gedaref State
100 90 100 90 80 80 70 70 60 60 50 50 Percent
40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year
Area (%) affected by drought : Blue Nile State Area (%) affected by drought : Gezira State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year
Area (%) affected by drought : Western Darfur State Area (%) affected by drought : South Darfur State
31 100 100 90 90 80 80 70 70 60 60 50 50 Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought : North Darfur State Area (%) affected by drought : Lakes State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year
Area (%) affected by drought : Bahr el Jabal State Area (%) affected by drought : Eastern Equatoria State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought : Upper Nile State Area (%) affected by drought : Western Bahr el Ghazal State
32 100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year Area (%) affected by drought : Unity State Area (%) affected by drought : Northern Bahr el Ghazal State
100 100 90 90 80 80 70 70 60 60 50 50
Percent 40 Percent 40 30 30 20 20 10 10 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year Year
Area (%) affected by drought : Jonglei State Area (%) affected by drought : Warrab State
100 90 80 70 60 50
Percent 40 30 20 10 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year
Area (%) affected by drought : Western Equatoria State
33 About ICARDA and the CGIAR
Established in 1977, the International Center for Agricultural Research in the Dry Areas (ICARDA) is one of 15 centers supported by the CGIAR. ICARDA’s mission is to contribute to the improvement of livelihoods of the resource-poor in dry areas by enhancing food security and alleviating poverty through research and partnerships to achieve sustainable increases in agricultural productivity and income, while ensuring the efficient and more equitable use and conservation of natural resources.
ICARDA has a global mandate for the improvement of barley, lentil and faba bean, and serves the non-tropical dry areas for the improvement of on-farm water use efficiency, rangeland and small-ruminant production. In the Central and West Asia and North Africa region, ICARDA contributes to the improvement of bread and durum wheats, kabuli chickpea, pasture and forage legumes, and associated farming systems. It also works on improved land management, diversification of production systems, and value-added crop and livestock products. Social, economic and policy research is an integral component of ICARDA’s research to better target poverty and to enhance the uptake and maximize impact of research outputs.
CGIAR is a global research partnership that unites organizations engaged in research for sustainable development. CGIAR research is dedicated to reducing rural poverty, increasing food security, improving human health and nutrition, and ensuring more sustainable management of natural resources. It is carried out by the 15 centers who are members of the CGIAR Consortium in close collaboration with hundreds of partner organizations, including national and regional research institutes, civil society organizations, academia, and the private sector. WWW.cgiar.org